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How to achieve product market fit: “The Superhuman Product Market Fit Engine”, a case study

Thomas Catnach, Product Lead, iWarranty

“It’s probably worth starting with what product market fit even is. And what it is is the number one reason why startups succeed and the lack of it, sadly, is also the number one reason why start ups fail.”

Rahul Vohra, Superhuman Founder and CEO

In 2017, Rahul Vohra was trying to ensure Superhuman’s product market fit.

The only thing more difficult than finding product market fit is defining product market fit.  How do you know exactly what it is? 

“Back then it was really tough to find a crystal clear definition of product market fit…Back then folks like Paul Graham, founder of Y Combinator, would say it’s when you made something that people want. Or Sam Altman would say it’s when users spontaneously tell other people to use your product.  But I think it’s Marc Andreesen…who has the most vivid definition.  He would say ‘You can always feel it when product market fit is not happening.  Customers aren’t quite getting value, users aren’t growing that fast, word of mouth is not spreading,  the press reviews are kind of bla and the sales cycle takes too damn long.  But you can always feel it when product market fit is happening.  Customers are buying as fast as you can add servers, you’re hiring sales and support as fast as you can, reporters are constantly calling you about your hot new thing, investors are staking out your house and money is piling up in your checking account.’”

Rahul Vohra, Superhuman Founder and CEO

All of the above definitions of product market fit are lagging indicators, i.e. you know when you’re there, but how do you know you’re getting there?  What if there was some way to measure that you were achieving product market fit before you achieved product market fit? 

Fortunately this question has been answered. In this guide we’re focusing on how you can implement the Superhuman product market fit engine, a classic case study to measuring leading indicators of product market fit.  We’ll walk you through how to implement a effective, iterative development cycle that drives a pre-PMF company towards successfully attaining product market fit. 

You require some active users in order to be able to execute this process. If you’re pre-launch it’s better to focus on potential user interviews. 

Jump to the step-by-step guide to running the Superhuman product market fit engine process

The Superhuman journey towards product market fit

What is Superhuman

Superhuman – founded in 2015 – is the world’s fastest email client. It’s designed to increase individual and team productivity by helping people manage their inboxes effectively.  Features include automatic email triage, response reminder nudges and snooze capability, phrase and email automation, and even advanced features such as read views, allowing Superhuman users to craft just in time messaging. 

Superhuman aims overall to help users avoid ‘The Three Sins of Email’: blocking their team, damaging their reputation or missing opportunities.  It’s raised over $125m and has a near unicorn valuation.

Superhuman is the fastest email experience ever made..our customers get through their inbox twice as fast as before, they reply to their most important emails sooner, and they save 3 hours or more every week

Rahul Vohra, Superhuman Founder and CEO

How the idea for the company emerged

The concept for Superhuman was born out of the CEO’s previous company, Rapportive, a Gmail plugin acquired by Linkedin in 2012.  This gave Rahul an insight into the problem space first hand, so you might think he was able to get straight out of the gate with a slam dunk product.  However building Superhuman was a slow process due to the scale of the ambition and the complexity of building an email client; and Rahul invested time constantly into understanding user needs and pain points.  

The first year of work was heavily focused on user interviews, preemptive cash raises, and no product was created.  Questions were conducted asynchronously on email, with an questionnaire onsite, followed by Rahul trading emails with folks who agreed to talk, ranging from ‘What email client do you use?’ to ‘What do you hate about it?’.  

This gave him validation for the initial concept and focus, with the major pieces of feedback being ‘It’s way too slow, and it takes way too much time’. Subsidiary feedback was around existing options not working offline and being major CPU hogs.

Year 2 was the year of building the product.  Fast forward to the summer of 2017, where before the Superhuman public launch, Rahul still wasn’t sure they’d nailed it. He was looking for a way to communicate internally his belief that the product wasn’t yet good enough, despite the amount of work that had already gone into it, and a way to uncover what was needed to take the product to the next level.  In short, Rahul didn’t believe in Superhuman’s product market fit, and he was looking for a way to communicate and codify achieving it.

This is when he created the Superhuman Product Market Fit (PMF) engine, to which a lot of Superhuman’s early success is attributed. The Superhuman Product Market Fit (PMF) engine was a codified process that guided Superhuman’s roadmap to improve the Superhuman product market fit score as well as their net promoter score, helping them acquire and retain paying customers as well as attract investors. It was a crucial step in Superhuman’s journey to near unicorn status, culminating most recently in a $75m Series C raise at a $825m valuation in 2021

Superhuman product market fit score
Superhuman’s product market fit (PMF) score improvement over time

“All I really want to do is make beautiful things which make people smile”

Rahul Vohra, Superhuman Founder and CEO

The Superhuman Product Market Fit Engine

“I set out looking for this way to define product market fit, for a metric to measure product market fit and then ultimately a methodology to systematically increase it.”

Rahul Vohra, Superhuman CEO

It was important to make PMF measurable, not only for future success but also to align the organization on the end goal.  Back in 2017 Rahul knew Superhuman was not ready to launch, but not everyone had the same opinion.

“Further compounding the pressure, as a founder, I couldn’t just tell the team how I felt. These super-ambitious engineers had poured their hearts and souls into the product. I had no way of telling the team we weren’t ready, and worse yet, no strategy for getting out of the situation — which is not something they would want to hear. I wanted to find the right language or framework to articulate our current position and convey the next steps that would get us to product/market fit, but was struggling to do so.”

Rahul Vohra, Superhuman CEO

So Rahul went looking for metrics.  

“My core insight came when I found Sean Ellis, who ran early growth at Dropbox, LogMeIn, and Eventbrite — and who coined the term “growth hacker”. Sean had found a leading indicator…”

Rahul Vohra, Superhuman CEO

“I’ve tried to make the [product market fit] concept less abstract by offering a specific metric for determining product/market fit. I ask existing users of a product how they would feel if they could no longer use the product. In my experience, achieving product/market fit requires at least 40% of users saying they would be “very disappointed” without your product. Admittedly this threshold is a bit arbitrary, but I defined it after comparing results across nearly 100 startups. Those that struggle for traction are always under 40%, while most that gain strong traction exceed 40%.”

Sean Ellis, The Start Up Pyramid

Rahul took the findings of the Sean Ellis test, and executed on its premise, feeling it offered a data driven, evidence backed approach to identifying PMF that he could rally the company around.  

The Superhuman team honed their product consistently until over 40% of their target group said they would be “very disappointed” if they could no longer use the product. This process is now called the Superhuman Product Market Fit Engine.

Rahul’s insight had been that the metric could be codified as a leading indicator and developed into a forward looking process to design success, rather than a lagging indicator identifying failure. The world found out what Rahul had done when he shared an article in 2018 describing what he had done. 

How to apply the Superhuman Product Market Fit Engine within your start up:

Superhuman product market fit virtuous cycle
The virtuous Superhuman Product Market Fit cycle

Survey – Gathering data from users

To make PMF measurable, Superhuman deployed a standardised survey containing 4 questions, which you can copy in your own internal processes:

  1. How would you feel if you could no longer use [product_name]
    • Very disappointed
    • Somewhat disappointed
    • Not disappointed
  2. What type of people do you think would most benefit from [product_name]?
    • Text response
  3. What is the main benefit you receive from [product_name]?
    • Text response
  4. How can we improve [product_name] for you?
    • Text response

It’s important however to understand the purpose behind the questions in order to be able to use and interpret the survey correctly.

The response to the first question gives you your overall PMF score. You want 40% or more of your target users to be “very disappointed” if they could no longer use your product.  

The 2nd and 3rd questions are designed to help you identify your target audience and develop a persona for them. If you listen to Rahul speak, you’ll hear him talk a lot about Nicole.  Nicole is Superhuman’s high-expectation customer (HXC) and she has very specific characteristics. 

The 4th question is designed to capture needs from that HXC profile to inform future roadmaps and ensure you’re prioritising the right things.

The survey should be short and must include only these questions and answer options.  Resist the urge to add more. 

Avoiding bias in your survey

When you send your first survey you should be aiming for 100 or more responses.  There is still value in the survey if you don’t have many users but the first time Rahul sent it out he got 100-200 responses. However –

“Smaller, earlier-stage startups shouldn’t shy away from this tactic — you start to get directionally correct results around 40 respondents, which is much less than most people think.”

Rahul Vohra, Superhuman CEO 

It is important to remember that if you only get a few responses you need to be careful when interpreting your data. Small data sets can be misleading, particularly if only the users who like your product are responding. Consider doing more customer development interviews if you don’t get many responses to access richer insights. 

If you already have thousands of users, consider sending your survey to a random sample every month or two. Sampling allows you to send the survey multiple times without sending it to any given user more than once. This allows you to track your PMF score over time, without falling foul of survivorship bias.  

Single PMF score
Single PMF score gives no indication of direction of travel

Tracking PMF over time provides quantitative measure of whether you are moving towards PMF

Analyse – Uncover the insights in the data

Now you have responses to your PMF survey you need to work out who your target customers are and how to satisfy them to improve your PMF. 

There are three steps to this analysis:

  1. Identify your high-expectation customer (HXC) profile 
  2. Understand why HXCs love your product
  3. Understand what stops HXCs loving your product

Identify your high–expectation customer (HXC) profile

Here you’re identifying who your product is for and who it isn’t for. This is a critical step as it tells you who your product should be trying to satisfy. If you don’t know who your target customer is it’s hard to push back on user feedback that isn’t relevant and you can’t prioritise your work. 

To identify your target users there are four steps:

  1. Segment the responses by how they responded to the first question
  2. Assign a persona to each response
  3. Filter responses by personas who would be “very disappointed” if they could no longer use your product (your HXC)
  4. Flesh out the profile of your high-expectation customer (HXC)

A: Segment the responses by how they responded to the first question

Superhuman’s initial data for “How would you feel if you could no longer use Superhuman?” showed the following: 

Initial data on Q: "How would you feel if you could no longer use Superhuman?"
Initial data on Q: “How would you feel if you could no longer use Superhuman?”

These initial numbers give you your PMF baseline and tell you much about how different types of users value your product. Here Superhuman saw that only 22% of their users would be “very disappointed” if they could no longer use the product. The first slice on the data indicated Superhuman’s product market fit score was 22%. Before diving into the feedback though, Rahul wanted to understand his users better.

B: Assign a persona to each response

Identifying and understanding the users who love your product at this stage is critical to prevent being misled by feedback from users who will never love your product. It also helps you build a vivid picture of your ideal users which you can share across your organisation.

Categorising each response into a persona is how you can start to segment your response data to access deeper insights.

Personas assigned to responses
Personas assigned to responses

C: Filter responses by personas who would be “very disappointed” if they could no longer use your product (your HXC)

For Superhuman, only 4 personas said they would be “very disappointed” if they had to stop using the product:

  • Founder
  • Manager
  • Executive
  • Business Development

These 4 target personas can now be used to filter the rest of the responses. The thinking here is to focus only on personas who have proven that they can be really excited by the product. Superhuman deliberately ignored personas that they weren’t sure would ever answer “very disappointed” to the first survey question.

When filtering all of the responses to only these target personas, the “very disappointed” percentage lifts from 22% to 32%. These personas combined are the segment to double down on – the basis for the HXC profile, and 32% was now the Superhuman product market fit score to raise.

Feedback filtered for personas that can be "very disappointed"
Feedback filtered for personas that can be “very disappointed”

D: Flesh out the profile of your high-expectation customer (HXC)

After categorising the responses into personas, the Superhuman team built a high-expectation customer (HXC) profile: 

“The high-expectation customer, or HXC, is the most discerning person within your target demographic. It’s someone who will acknowledge—and enjoy—your product or service for its greatest benefit,” 

Julie Supan, worked on the launches of YouTube, Airbnb, Dropbox and Discord

The Superhuman team targeted only those users who would be “very disappointed” and analysed their responses to “What type of people do you think would most benefit from Superhuman?” to create their HXC profile. This is crucial to ensure you’re building the right thing for the right person.  Invest time in writing up your HXC and know her in detail. You can do this by running generative research on users who match your HXC profile.

Superhuman’s HXC profile

Nicole is a hard-working professional who deals with many people. For example, she may be an executive, founder, manager, or in business development. Nicole works long hours, and often into the weekend. She considers herself very busy, and wishes she had more time. Nicole feels as though she’s productive, but she’s self-aware enough to realize she could be better and will occasionally investigate ways to improve. She spends much of her work day in her inbox, reading 100–200 emails and sending 15–40 on a typically day (and as many as 80 on a very busy one).

Nicole considers it part of her job to be responsive, and she prides herself on being so. She knows that being unresponsive could block her team, damage her reputation, or cause missed opportunities. She aims to get to Inbox Zero, but gets there at most two or three times a week. Very occasionally — perhaps once a year — she’ll declare email bankruptcy. She generally has a growth mindset. While she’s open-minded about new products and keeps up to date with technology, she may have a fixed mindset about email. Whilst open to new clients, she’s skeptical that one could make her faster.

Now that you’ve identified your HXC the next step is to establish what is holding them back by answering two questions: 

  • Why do HXCs love your product?
  • What stops HXCs from loving your product? 

Answering these questions will give you a roadmap to reach and exceed the target 40% product market fit threshold. 

Why do HXCs love your product?

To answer why people love your product, you can look at your users who would be “very disappointed” again and analyse their response to the third question “What is the main benefit you receive from Superhuman?” 

Superhuman used a word cloud to visualise responses from HXCs quickly:

Word cloud of HXC's responses
Word cloud of HXC’s responses

These users wanted to see even more speed, shortcuts and automation. 

This type of analysis also helps you identify your value proposition. Consider sharing your findings within your organisation, particularly with marketing and acquisition teams, to help them understand your customer better.

What stops HXCs loving your product? 

Now you know why some people love your product you can investigate what is holding other people back. It may seem counterintuitive, but you’re not going to look at the data from the users who responded “not disappointed” to question one. As Rahul explains “Politely disregard those who would not be disappointed without your product. They are so far from loving you that they are essentially a lost cause.”

Looking only at the “somewhat disappointed” users, you need to analyse the response to the fourth question in the survey, “How can we improve Superhuman for you?” The Superhuman team went a step further than this and segmented again to only users who saw speed as the main product benefit. Speed is the key product benefit that users who already love the product identified and also where Superhuman could differentiate themselves in a market already full of email clients. 

“Somewhat disappointed users for whom speed was not the main benefit… we opted to politely disregard them, as our main benefit did not resonate. Even if we built everything they wanted, they would be unlikely to fall in love with the product.”

Rahul Vohra – Founder and CEO of Superhuman

If your “somewhat disappointed” users all love your product for the same reason it isn’t necessary to do this step, but if some of them love your product for reasons that aren’t core to your business, consider disregarding their feedback.

Reasons that users don’t love Superhuman yet

These users wanted to see a mobile app, more integrations and better attachment handling

Improve – Build a roadmap to satisfy your target customers’ needs

Next you need to build a roadmap that improves your PMF survey score. By taking the time to establish your target audience (HXC), what they love, and what is holding the back, you have built a detailed answer to two core questions:

  • How do we continue to improve on our core strengths?
  • How do we convert moderately satisfied users into users who love the product?

For Superhuman, this looked something like this:

  • Improve core strengths
    • More speed
    • More shortcuts
    • More automation
  • Convert moderately satisfied users
    • Mobile app
    • Integrations
    • Attachment handling

It’s important to give attention to both of these areas as you need to both improve your position with your already highly engaged users as well as convert more moderately satisfied users. Don’t forget to double down on your strengths at the same time as you’re expanding your customer pool. 

Start with a 50:50 split of work in your roadmap and then adapt this based on your current “very disappointed” percentage. Use the routine survey (automated if possible) to tweak and fine tune your roadmap as you get more data. 

Having feedback on a regular basis is important to measure the impact of your work. Without a way to regularly measure the impact, you won’t know if the changes you’re making are improving your product or not. Make the PMF survey routine. 

The process above improved the Superhuman product market fit score from 33% to 58% within 9 months and they continue to use it to this day. Throughout the process, Superhuman’s customers became noticeably more vocal about how much they loved the product, and as an added benefit raising capital also became easier. 

PMF increases over 9 months of working engine

Final thoughts

Get started

The most important step is to get started, send out your survey to users and start using the feedback as soon as you can. There are several easy-to-use, free survey tools such as Google Forms which can help you get going straight away. You can also use Sean Ellis’ PMF survey for free.

Pivot if necessary

The Superhuman product market fit engine is mainly a product refinement tool which will help you take iterative steps from an already good base. If you have hundreds of responses of which very few would be “very disappointed” if your product were to be taken away it may be time to think more broadly about your value proposition and consider a pivot. Pivoting is a radical step that the Superhuman product market fit engine is not really designed to deal with – Inspired is a great resource to get you started in the right direction. 

Iterate as you go

The Superhuman product market fit engine is an iterative process that should be repeated regularly. Repeating the process tells you if you’re getting better and how. It is important to use other data sources as well, such as behavioural data like retention, as this will paint a more vivid picture of how your product is improving. 

Summary: Superhuman Product Market Fit Engine

The Superhuman product market fit engine is a powerful, proven tool to improve any early-stage product, helping the team building it to deeply understand their users and build a product that meets – and exceeds – user needs. 

To set up the survey you need to regularly ask users 4 key questions covering what they think of the product as it currently stands, how it could be improved and who the product is for. Next, you analyse the responses to establish who your target customer is, as well as working out why some users love your product and how you can get more people to love your product. Finally, you implement a roadmap that addresses both the needs of users who already love your product and the needs of those who could love your product.


More case studies:


What is product market fit? 

Product market fit has been defined as the degree to which a product or service meets market demand.  Qualitatively it’s described as the moment at which your product sells itself – users start to recommend your product, growth accelerates, investors become interested in your business, and your work internally becomes about servicing the demand for your product, rather than about persuading folks to buy your product.  According to the Sean Ellis test, a popular test for early stage startups looking to achieve product market fit, it’s when 40% of surveyed users say that they would be ‘very disappointed’ if your product or service no longer exists. 

How to measure product market fit?

There are various ways to measure product market fit. In terms of a robust, data driven, research based test to determine product market fit, the Sean Ellis test is popular with early stage start ups. According to the Sean Ellis test, if over 40% of users surveyed respond ‘very disappointed’ to the question ‘How disappointed would you be if the product or service no longer existed?’, then product market fit has been attained. For later stage companies various other hygiene and financial metrics can be used to determine the health of the company.  Traffic metrics, such as bounce rate, time on site, pages per visit, returning visitors indicate whether your site is valuable to users and whether they return and spend time on it.  User satisfaction metrics, such as NPS and CSAT give direct feedback on whether your users find you valuable.   Financial metrics such as customer lifetime value indicate whether your customers and business model have sufficient synergy to the degree that users are prepared to pay sufficiently for it to cover your acquisition and operating costs.  Other finance metrics, where costs and growth are analyzed, such as the Rule of 40 are popular in SaaS companies.

How to determine product market fit?

Many product market fit metrics are lagging indicators, i.e. you know when you have arrived but not if you are getting there. The Sean Ellis test is effective in this regard, since via routine customer surveys you can ascertain if you are progressing towards product market fit. Under the Sean Ellis test if 40% or more of users surveyed reply that they would be ‘very disappointed’ if your product or service no longer existed, you have attained product market fit. 

At what stage does one find product market fit?

Typically technology companies look to attain product market fit during early stage or prior to Series A fund raises.  This is because attainment of product market fit is usually a prerequisite to raising cash intended for scaling up the business. 

What is the first stage in forming a product market fit hypothesis?

Target user interviews in a target market is a useful first stage to forming a product market fit hypothesis.  By interviewing possible future customers within your chosen industry about the limitations of products or services they employ, this will provide you with qualitative insights into how to achieve product market fit. Additionally often founders have experienced first hand the pain points they are looking to solve themselves or have worked in their target industry for some period of time to be able to understand the landscape well. 

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